Nucleosome reorganisation in breast cancer tissues

Background Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes. Results We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5–10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X. Conclusions Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-024-01656-4.


Supplementary Figure
. Estrogen receptor  is undetectable in patient P3.Two breast cancer cell lines, A549 (ER⍺ -ve) and T47D (ER⍺ +ve), have been used as a negative and positive control correspondingly.Cells from A549, T47D and tumour tissue of patient P3 were lysed in RIPA buffer.Protein concentrations were measured and adjusted, and 60 µg of protein was loaded per lane.ER⍺ and Tubulin expression were visualized using immunoblotting.The averaged genome-wide distribution of occurrences of nucleosome dyad-dyad distances (top graph), and the linear regression calculating NRL based on the summits of the peaks of this distribution (bottom).
Relationship between gene expression and nucleosome density in gene body.A) Scatter plot of nucleosome density in tumour and normal tissues shows a bimodal distribution, with a fraction of genes showing significant change of nucleosome density.B) Analysis of 88 genes which show >2-fold increase of nucleosome density across their gene body in tumour vs normal breast tissue of patient P2.Correlation between gene expression in breast cancer patients reported by the TCGA consortium and nucleosome density in tumour breast tissues of patient P2 from this study.Nucleosome density of a given gene was defined as the number of mapped MNase-seq DNA fragments divided by the gene length.
The averaged genome-wide distribution of occurrences of nucleosome dyad-dyad distances (top graph), and the linear regression calculating NRL based on the summits of the peaks of this distribution (bottom). .Calculation of genome-wide NRL for sample P3 N H3.The averaged genome-wide distribution of occurrences of nucleosome dyad-dyad distances (top graph), and the linear regression calculating NRL based on the summits of the peaks of this distribution (.Calculation of genome-wide NRL for sample P3 T H3.The averaged genome-wide distribution of occurrences of nucleosome dyad-dyad distances (top graph), and the linear regression calculating NRL based on the summits of the peaks of this distribution of genome-wide NRL for sample P4 T MNase.The averaged genome-wide distribution of occurrences of nucleosome dyad-dyad distances (top graph), and the linear regression calculating NRL based on the summits of the peaks of this distribution of genome-wide NRL for sample P4 N H3.The averaged genome-wide distribution of occurrences of nucleosome dyad-dyad distances (top graph), and the linear regression calculating NRL based on the summits of the peaks of this distribution of genome-wide NRL for sample P4 T H3.The averaged genome-wide distribution of occurrences of nucleosome dyad-dyad distances (top graph), and the linear regression calculating NRL based on the summits of the peaks of this distribution (bottom).Supplementary Figure S18.Distribution of DNA fragments protected from digestion in individual patients and averaged across all patients.A) and B) MNase-seq; C) and D) MNase-assisted H3 ChIP-seq; E) cfDNA from blood plasma.N represent normal breast tissue samples, T represents tumour breast tissue.P1, P2, P3, P4 denote different patients.F) Same as A-E, averaged across all patients in each condition.of DNA fragments protected from digestion, averaged across all samples in a given condition based on MNase-seq, MNase-assisted H3 ChIP-seq from this study and cfDNA sequencing from Snyder et al (Snyder et al. 2016).A) DNA fragment size distributions inside A and B compartments in MCF7 cells and genome-wide.B-C) DNA fragment size distributions inside different types of repetitive and nonrepetitive genomic regions indicated in the figure.D) DNA fragment size distributions inside 10-kb genomic regions with high and low GC content.The solid lines represent normal samples and dashed line represents tumour samples. .Distribution of DNA fragment sizes protected from digestion inside genomic regions enriched with H3K9me2/3 histone modifications.A) DNA fragment size distribution based on MNase-seq, MNase-assisted H3 ChIP-seq and cfDNA sequencing (Snyder et al. 2016) inside regions enriched with H3K9me3 in HMEC cells (healthy breast cells) (Zhang et al. 2020).B) DNA fragment sizes based on MNase-seq, MNase-assisted H3 ChIP-seq and cfDNA sequencing inside regions enriched with H3K9me3 in MCF-7 cells (Dunham et al. 2012).C) DNA fragment sizes based on MNase-seq, MNase-assisted H3 ChIP-seq and cfDNA sequencing inside regions enriched with H3K9me2 in MCF-7 cells (Dunham et al. 2012).The solid line corresponds to the averaged profile across all samples in a given condition.The red/grey clouds around the average line show the corresponding standard deviation of averaging.

Supplementary Figure S6. Calculation of the genome-wide NRL for sample P1 N H3. The
Supplementary Figure S5.Calculation of the genome-wide NRL for sample P1 T MNase.The averaged genome-wide distribution of occurrences of nucleosome dyad-dyad distances (top graph), and the linear regression calculating NRL based on the summits of the peaks of this distribution (bottom).

Supplementary Figure S7. Calculation of the genome-wide NRL for sample P1 T H3. The
Supplementary FigureS8.Calculation of the genome-wide NRL for sample P2 N MNase.